Thesis Abstract

Abstract
Sentiment Analysis of Song Lyrics Using Machine Learning Techniques This thesis explores the application of machine learning techniques in analyzing sentiment in song lyrics. The project aims to develop a system that can automatically assess the emotional tone and sentiment expressed in song lyrics, providing valuable insights for various applications in the music industry, social media analysis, and emotional content understanding. The research begins with a comprehensive introduction, providing background information on sentiment analysis, machine learning, and the significance of analyzing sentiment in song lyrics. The problem statement highlights the challenges of manually analyzing large volumes of song lyrics and the need for automated tools to extract sentiment effectively. The objectives of the study include developing a sentiment analysis model tailored for song lyrics, evaluating its performance, and exploring potential applications in music recommendation systems and emotional content analysis. The limitations of the study are discussed, focusing on the challenges of accurately interpreting sentiment in creative and metaphorical language often found in song lyrics. The scope of the research covers the development and evaluation of the sentiment analysis model using a dataset of diverse song lyrics from various genres and time periods. The significance of the study lies in its potential to enhance music recommendation systems, improve sentiment analysis tools, and deepen our understanding of the emotional content conveyed through music. The structure of the thesis is outlined, detailing the organization of chapters and sub-sections, including the literature review, research methodology, discussion of findings, and conclusion. Definitions of key terms related to sentiment analysis, machine learning, and song lyrics are provided to ensure clarity and understanding throughout the document. The literature review chapter examines existing research on sentiment analysis, machine learning applications in music, and methodologies for analyzing emotional content in text data. Key themes such as feature extraction, sentiment classification techniques, and sentiment lexicons are explored to provide a comprehensive background for the research. The research methodology chapter outlines the data collection process, feature engineering techniques, machine learning algorithms, evaluation metrics, and experimental setup used to develop and evaluate the sentiment analysis model. The chapter also discusses the preprocessing steps for cleaning and tokenizing song lyrics, feature selection methods, and model training procedures. The discussion of findings chapter presents the results of the sentiment analysis model evaluation, including accuracy, precision, recall, and F1-score metrics. The chapter also analyzes the performance of the model across different genres, sentiment categories, and lyric lengths, highlighting strengths, limitations, and areas for further improvement. In conclusion, this thesis summarizes the key findings, contributions, and implications of the research on sentiment analysis of song lyrics using machine learning techniques. The study demonstrates the feasibility of automating sentiment analysis in song lyrics and provides insights for future research directions in music sentiment analysis and emotional content understanding.

Thesis Overview

The project titled "Sentiment Analysis of Song Lyrics Using Machine Learning Techniques" aims to explore and analyze the sentiment expressed in song lyrics through the application of machine learning algorithms. Sentiment analysis, also known as opinion mining, is a field of study that involves the use of natural language processing, text analysis, and computational linguistics to identify and extract subjective information from text data. In the context of this project, sentiment analysis will be applied to song lyrics to uncover the underlying emotions, themes, and sentiments conveyed in the songs. The use of machine learning techniques in sentiment analysis offers a powerful and efficient way to process and analyze large volumes of textual data. By training machine learning models on labeled datasets of song lyrics, the project aims to develop a sentiment analysis system that can accurately classify and interpret the emotional content of the lyrics. Through this analysis, valuable insights can be gained into the themes and sentiments prevalent in different genres of music, as well as the emotional impact of specific songs on listeners. The research will begin with an exploration of the existing literature on sentiment analysis, machine learning, and related studies in the field of music analysis. This literature review will provide a comprehensive overview of the current state-of-the-art techniques and methodologies used in sentiment analysis, as well as insights into the challenges and opportunities in analyzing song lyrics. The research methodology will involve collecting and preprocessing a large dataset of song lyrics from various genres and artists. Feature extraction techniques will be applied to represent the textual data in a format suitable for machine learning models. Different machine learning algorithms, such as support vector machines, neural networks, and decision trees, will be trained and evaluated to determine the most effective approach for sentiment analysis of song lyrics. The findings of the study will be presented and discussed in detail, highlighting the performance of the machine learning models in accurately classifying the sentiment of song lyrics. The implications of the results will be discussed in terms of understanding the emotional content of music and its potential applications in music recommendation systems, sentiment-based music playlists, and emotional analysis of music trends. In conclusion, the project will provide valuable insights into the sentiment expressed in song lyrics using machine learning techniques, contributing to the fields of sentiment analysis, music analysis, and computational linguistics. The research findings will have implications for understanding the emotional impact of music on listeners and enhancing the user experience in music streaming platforms and digital music services.